





'''simple docstring'''






from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
					import timm


if is_torch_available():
					from torch import Tensor
class 		lowerCamelCase	(   _A   , _A							):
	snake_case_   = "pixel_values"
	snake_case_   = False
	snake_case_   = TimmBackboneConfig



	def __init__( self		,   a_		,   **a_						):
			requires_backends(self		,   "timm"						)
			super().__init__(a_						)
			lowerCAmelCase  :     List[str]       						= config

			if config.backbone is None:
					raise ValueError("backbone is not set in the config. Please set it to a timm model name."						)

			if config.backbone not in timm.list_models():
					raise ValueError(F'''backbone {config.backbone} is not supported by timm.'''						)

			if hasattr(a_		,   "out_features"						) and config.out_features is not None:
					raise ValueError("out_features is not supported by TimmBackbone. Please use out_indices instead."						)

			lowerCAmelCase  :     int       						= getattr(a_		,   "use_pretrained_backbone"		,   a_						)
			if pretrained is None:
					raise ValueError("use_pretrained_backbone is not set in the config. Please set it to True or False."						)

			# We just take the final layer by default. This matches the default for the transformers models.
			lowerCAmelCase  :     Tuple       						= config.out_indices if getattr(a_		,   "out_indices"		,   a_						) is not None else (-1,)

			lowerCAmelCase  :     Union[str, Any]       						= timm.create_model(
			    config.backbone		,   pretrained=a_		,   features_only=config.features_only		,   in_chans=config.num_channels		,   out_indices=a_		,   **a_		,   )
			# These are used to control the output of the model when called. If output_hidden_states is True, then
			# return_layers is modified to include all layers.
			lowerCAmelCase  :     Dict       						= self._backbone.return_layers
			lowerCAmelCase  :     Optional[Any]       						= {layer["module"]: str(a_						) for i, layer in enumerate(self._backbone.feature_info.info						)}
			super()._init_backbone(a_						)



	@classmethod
	def 	_lowerCamelCase     ( cls		,   a_		,   *a_		,   **a_						):
			requires_backends(cls		,   ["vision", "timm"]						)
			from ...models.timm_backbone import TimmBackboneConfig

			lowerCAmelCase  :     Union[str, Any]       						= kwargs.pop("config"		,   TimmBackboneConfig()						)

			lowerCAmelCase  :     str       						= kwargs.pop("use_timm_backbone"		,   a_						)
			if not use_timm:
					raise ValueError("use_timm_backbone must be True for timm backbones"						)

			lowerCAmelCase  :     str       						= kwargs.pop("num_channels"		,   config.num_channels						)
			lowerCAmelCase  :     Optional[Any]       						= kwargs.pop("features_only"		,   config.features_only						)
			lowerCAmelCase  :     Union[str, Any]       						= kwargs.pop("use_pretrained_backbone"		,   config.use_pretrained_backbone						)
			lowerCAmelCase  :     Dict       						= kwargs.pop("out_indices"		,   config.out_indices						)
			lowerCAmelCase  :     str       						= TimmBackboneConfig(
			    backbone=a_		,   num_channels=a_		,   features_only=a_		,   use_pretrained_backbone=a_		,   out_indices=a_		,   )
			return super()._from_config(a_		,   **a_						)



	def 	_lowerCamelCase     ( self		,   a_						):
			pass





	def 	_lowerCamelCase     ( self		,   a_		,   a_=None		,   a_=None		,   a_=None		,   **a_						):
			lowerCAmelCase  :     Tuple       						= return_dict if return_dict is not None else self.config.use_return_dict
			lowerCAmelCase  :     Union[str, Any]       						= (
			    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
			)
			lowerCAmelCase  :     str       						= output_attentions if output_attentions is not None else self.config.output_attentions

			if output_attentions:
					raise ValueError("Cannot output attentions for timm backbones at the moment"						)

			if output_hidden_states:
					# We modify the return layers to include all the stages of the backbone
					lowerCAmelCase  :     int       						= self._all_layers
					lowerCAmelCase  :     Optional[Any]       						= self._backbone(a_		,   **a_						)
					lowerCAmelCase  :     List[Any]       						= self._return_layers
					lowerCAmelCase  :     List[Any]       						= tuple(hidden_states[i] for i in self.out_indices						)
			else:
					lowerCAmelCase  :     Tuple       						= self._backbone(a_		,   **a_						)
					lowerCAmelCase  :     Any       						= None

			lowerCAmelCase  :     Optional[Any]       						= tuple(a_						)
			lowerCAmelCase  :     Tuple       						= tuple(a_						) if hidden_states is not None else None

			if not return_dict:
					lowerCAmelCase  :     List[str]       						= (feature_maps,)
					if output_hidden_states:
							lowerCAmelCase  :     Dict       						= output + (hidden_states,)
					return output

			return BackboneOutput(feature_maps=a_		,   hidden_states=a_		,   attentions=a_						)










'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase									=   logging.get_logger(__name__)

lowerCAmelCase									=   {
    """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json""",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class 		lowerCamelCase	(   _A							):
	snake_case_   = "gpt_neox"



	def __init__( self		,   a_=50_432		,   a_=6_144		,   a_=44		,   a_=64		,   a_=24_576		,   a_="gelu"		,   a_=0.25		,   a_=10_000		,   a_=0.0		,   a_=0.0		,   a_=0.1		,   a_=2_048		,   a_=0.02		,   a_=1e-5		,   a_=True		,   a_=0		,   a_=2		,   a_=False		,   a_=True		,   a_=None		,   **a_		,   ):
			super().__init__(bos_token_id=a_		,   eos_token_id=a_		,   **a_						)
			lowerCAmelCase  :     Dict       						= vocab_size
			lowerCAmelCase  :     str       						= max_position_embeddings
			lowerCAmelCase  :     int       						= hidden_size
			lowerCAmelCase  :     Optional[Any]       						= num_hidden_layers
			lowerCAmelCase  :     int       						= num_attention_heads
			lowerCAmelCase  :     Any       						= intermediate_size
			lowerCAmelCase  :     Tuple       						= hidden_act
			lowerCAmelCase  :     Any       						= rotary_pct
			lowerCAmelCase  :     str       						= rotary_emb_base
			lowerCAmelCase  :     Tuple       						= attention_dropout
			lowerCAmelCase  :     Optional[int]       						= hidden_dropout
			lowerCAmelCase  :     int       						= classifier_dropout
			lowerCAmelCase  :     str       						= initializer_range
			lowerCAmelCase  :     str       						= layer_norm_eps
			lowerCAmelCase  :     List[Any]       						= use_cache
			lowerCAmelCase  :     Dict       						= tie_word_embeddings
			lowerCAmelCase  :     Any       						= use_parallel_residual
			lowerCAmelCase  :     Union[str, Any]       						= rope_scaling
			self._rope_scaling_validation()

			if self.hidden_size % self.num_attention_heads != 0:
					raise ValueError(
					    "The hidden size is not divisble by the number of attention heads! Make sure to update them!"						)





	def 	_lowerCamelCase     ( self						):
			if self.rope_scaling is None:
					return

			if not isinstance(self.rope_scaling		,   a_						) or len(self.rope_scaling						) != 2:
					raise ValueError(
					    "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
					    F'''got {self.rope_scaling}'''						)
			lowerCAmelCase  :     int       						= self.rope_scaling.get("type"		,   a_						)
			lowerCAmelCase  :     int       						= self.rope_scaling.get("factor"		,   a_						)
			if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
					raise ValueError(
					    F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''						)
			if rope_scaling_factor is None or not isinstance(a_		,   a_						) or rope_scaling_factor <= 1.0:
					raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''						)



